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Record W4318716759 · doi:10.4018/ijpada.316183

The Place of Smart Occupational Health and Safety in Smart City Design

2023· article· en· W4318716759 on OpenAlexfundno aff
Marı́a-Isabel Sánchez-Segura, Germán-Lenin Dugarte-Peña, Antonio de Amescua Seco, Fuensanta Medina‐Domínguez, Eugenio López-Almansa, Rosa Menchén Viso

Bibliographic record

VenueInternational Journal of Public Administration in the Digital Age · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
FundersUniversidad Carlos III de MadridInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du TravailComunidad de Madrid
KeywordsSmart cityContext (archaeology)Architectural engineeringComputer scienceDigital transformationRisk analysis (engineering)Order (exchange)BusinessComputer securityEngineeringInternet of ThingsWorld Wide Web

Abstract

fetched live from OpenAlex

Smart cities are a very clear example of complex systems, and their development focuses on the use of technology to transform every aspect of society and embrace the complexity of these transformations in order to promote the well-being and safety of the people who inhabit these cities. One essential, but often implicit, aspect that must be considered in the design of a smart city is occupational health and safety (OHS). After identifying a significant number of OHS issues that must be effectively addressed, a prospective analysis reveals that there is still an existing gap to be filled in the context of smart city design: an explicit guarantee of safety for workers in uncertain environments open to constant digital transformation changes. In this article, the authors present the VENTURA2020 model, an architectural capabilities-driven model that describes the main aspects to be taken into account to integrate smart OHS into smart city design.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.176
GPT teacher head0.479
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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